Forecast Models for Urban Extreme Temperatures (KATHAMNDU

REGION AS A CASE STUDY)

* Laxman Basnet

Dr. K.N. Modi University, Newai Rajasthan-304021, India

Abstract: The climatic signature of global warming is both local and global. The forcing by increasing greenhouse gases is global, so there is clearly a global component to the climatic signature. Moreover, the damaging impacts of global warming are manifesting themselves around the world in the form of extreme weather events like storms, tornadoes, floods and droughts, all of which have been escalating in frequency and intensity. Furthermore, it is a well-known fact that there is high degree of uncertainty surrounding projections of basic climate variables, such as temperature and precipitation. However, numerous authors have explored many of these effects individually and have begun exploring the interactions between climate change-induced impacts in different sectors of urban activities. Therefore, it is safe to say that an attempt to conduct a definitive, comprehensiveanalysis of all the potential impacts of climate change on the urban structure is premature at present. This communication attempts to examine the trends in maximum monthly urban temperature fluctuations. Analysis reveals increasing trends in urban temperature fluctuations showing effect of Kathamndu industrializations. Forecast models also suggest future scenario with respect to occurrence of extreme temperature. The analysis carried out in this work would be useful for urban planners for sustainable future development, economists and environmentalists etc.

Keywords: Global warming, Temperature fluctuations, Greenhouse gases, Climate, ACF and PACF

I. Introduction

Global warming (GW) is now an accepted fact that is influencing the lower atmosphere as well as the oceans of the globe [1] and that the climate parameters remain in dynamic equilibrium [2]. In addition, the dynamics of the climate system is chaotic [3-4]. Thus for a better planning, urban planners need to have a clear insight of the situation in future. This requires plausible forecasts of future population and other urban parameters. Fortunately, over the years, researchers have successfully developed sophisticated tools for obtaining better forecasts to support urban planning and management. In recent years, increasing number of studies has appeared dealing with the impact of intense heat on health of urban dwellers [5]. Here, we are not attempting to make a comprehensive assessment of the scientific literature corresponding to climate change. However, to mention some, are the significant correlation between eleven years sun sunspots cycles and ozone layer depletion over arctic region determined by [6-7], the determination of the presence of positive trend in global warming making the quantity of seawater increase due to increasing input from the lakes, underground water, and polar region glaciers and the acceleration in its fluctuations of the GW [8]. Situation in different regions can differ because of different structure, climatic conditions and different conditions of the oceans but the basic facts remain the same. However, the consequences can differ [9]. Urban structures parameters, for example, thermal and radioactive properties of the artificial surfaces modify the climate at both city scale and local scale. The main influence of the cities on microclimate is the heat island caused by radioactive trapping in urban materials. To predict overheating periods within the urban region, this communication attempts to give short- and long-term forecasts of urban extreme temperatures. Long-term forecasts are specially used by urban planners in designing of urban reserve areas and are more importantly, for sustainable urban development. The use of time series provided by modern remote sensing platforms, in particular improves the quality of derived ecological indicators. These indicators can be generated on a global scale from optical/thermal remote sensing data. The quality of ecological indicators, especially in complex terrain, is determined by thorough pre- and postprocessing of the data in order to minimize artifacts in the resulting maps. The MODIS sensor is currently the optimal match between temporal and spatial resolution and is an excellent data source for both local and global change research. The paper is organized as follows.